428 S Shaw LN
East Lansing, Michigan, USA
I am Yihua Zhang (张逸骅), a first-year Ph.D. student from OPTML Group at Michigan State University, supervised by Prof. Sijia Liu. My research focuses on the trustworthy and scalable ML algorithms. In general, my research spans the areas of machine learning (ML)/deep learning (DL), optimization theory, computer vision, and security. These research topics provide a solid foundation for my current and future research: Making AI system safe and scalable. My research on these two goals are intervened and can be summarized as the following two perspectives:
Algorithmic perspective: This line of research designs the scalable and theoretically-grounded machine learning algorithms subject to real-life constraints, e.g., computation/communication overhead, robustness, fairness, and interpretability.
Application perspective: This line of research tackles the domain-specific challenges to achieve scalable and trustworthy AI, e.g., robustness enhancement, fairness promotion, data privacy protection, and model compression.
|Sep 14, 2022||Two first-authored papers accepted in NeurIPS 2022!|
|Jul 26, 2022||Best Paper Runner-Up Award of UAI’22|
|May 31, 2022||One paper accepted in UAI 2022!|
|May 15, 2022||One paper accepted in ICML 2022!|
|Apr 20, 2022||I will serve as the student chair of at the ICML workshop New Frontiers in Adversarial Machine Learning !|
NeurIPS’22Advancing Model Pruning via Bi-level OptimizationIn Thirty-sixth Conference on Neural Information Processing Systems 2022
NeurIPS’22Fairness ReprogrammingIn Thirty-sixth Conference on Neural Information Processing Systems 2022
UAI’22Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleIn Uncertainty in Artificial Intelligence 2022
ICML’22Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level OptimizationIn Proceedings of the 39th International Conference on Machine Learning 2022
CVPR’22Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for FreeIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022